Table of Content

25 December 2023, Volume 51 Issue 12

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2023, 51(12):  0. 

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Mechanical Engineering

Research on Optimized Teaching Strategy and BPNN-DMPs Trajectory Learning Model of Massage Robot



ZHAI Jingmei, LU Dongwei

2023, 51(12):  1-8.  doi:10.12141/j.issn.1000-565X.230027

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For the individual difference of faces which are the operation object of massage robot, the dynamic motion primitives (DMPs) model was used to generalize the posture trajectory and force trajectory. Firstly, in order to improve the learning accuracy of DMPs, the study proposed an optimized teaching strategy. Based on the Mediapipe feature points in the massage area, the similarity between the operating objects was calculated to optimize the learning objects. Secondly, Gaussian mixture regression (GMR) was introduced, and the algorithm integrated multiple massage information to enhance learning ability. Finally, a back-propagation neural network (BPNN) model was constructed to fit the forced term of DMPs algorithm, which fundamentally changes the limitations of the original model. The experiment shows that the average errors of position and attitude of BPNN-DMPs model are reduced by 44.1% and 54.5%, 44.1% and 54.5%, 29.7% and 46.4% respectively, compared with DMPs, MDMPs and SADMPs algorithms without increasing the running time. Gaussian mixture regression can integrate multiple trajectory patterns and the implementation effect of the optimized teaching strategy is significant. Compared with the non-optimized object, the average errors of the position and posture of the face experiment are reduced by 52.3% and 70.2%, and the standard deviation is reduced by 46.3% and 71.1%. The average errors of position, posture and force in the back experiment decrease by 27.7%, 66.7% and 24.1%, and the standard deviation decreases by 25.7%, 54.4% and 44.1%.

Learning and Generalization of Dual-Robot Cooperative Handling Trajectory Based on Dynamic Movement Primitives



CAO Xuepeng, WANG Deshuo, FENG Yanli, et al

2023, 51(12):  9-20.  doi:10.12141/j.issn.1000-565X.230013

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Aiming at the problems of complex trajectory learning and lack of coordination constraint analysis when a dual-robot collaborative system performs humanoid tasks with strong coordination constraints, this paper proposed a dual-robot cooperative handling trajectory learning and generalization method based on dynamic movement primitives (DMPs). Firstly, starting from the dual-robot cooperative handling task, the coordination constraints of the dual-robot were analyzed, and the motion constraint model of the dual-robot was established. Then, the robot motion trajectory was decoupled into position trajectory and orientation trajectory, and the quaternion was used to realize the non-singular description of the orientation trajectory. And the dynamic movement primitives model of position trajectory and orientation trajectory were established respectively. They were combined with the dual robot motion constraint model and DMPs model, and the dual-robot movement trajectory was obtained, taking into account their respective task requirements and relative pose constraints. Finally, the simulation and experiments of the cooperative handling trajectory of the two robots were carried out. The results show that: using the learning and generalization method of the dual-robot cooperative handling trajectory, when the starting and ending states are changed, the position errors of start point and end point of the dual-robot cooperative handling with the fixed orientation are 0.029 2 mm and 0.112 7 mm respectively; the position errors of start point and end point of variable orientation coordinated handling are 0.032 3 mm and 0.113 1 mm respectively; and the quaternion orientation errors of the end point are 0.001 4, 0.002 7, 0.001 8, 0.003 0, indicating that the cooperative handling trajectory learning and generalization method has high motion control accuracy; even if the task parameters of the starting and ending are changed, the generalized trajectory can still ensure the accessibility of the target, which verified the scientificity and effectiveness of the proposed dual-robot coordination motion trajectory control strategy. The method proposed in this paper can effectively learn the human handling process and can accurately generalize new motion trajectories. It realizes the dual-robot coordinated motion and has important engineering application value.

Optimized Design of the Main Structure of a Wall-Climbing Robot for Bridge Detection Based on Negative Pressure Adsorption



HUANG Haixin, WANG Zheng, CHENG Shoushan, et al

2023, 51(12):  21-33.  doi:10.12141/j.issn.1000-565X.220695

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To address the challenging task of inspecting hard-to-reach areas, such as high piers and the bottom of bridges, the paper developed a wall-climbing robot for bridge disease detection based on negative pressure adsorption. For the robot’s own adsorption stability, this paper established and derived a formula for calculating the adsorption force index under conditions of anti-slip and anti-overturning, based on which the minimum adsorption force required by the robot to achieve stable wall adsorption at all angles was determined. The results show that to ensure the reliable operation of the robot, the adsorption module needs to provide 53.0 N adsorption force. The preliminary design of the centrifugal impeller was formulated based on empirical principles, followed by fluid mechanics simulation and response surface optimization of the impeller basin using Fluent. An evaluation function, comprising adsorption force and torque, was established to optimize the impeller design parameters to maximize the comprehensive evaluation function value of the adsorption module. Compared to the initial design scheme, the optimized design achieved a 3.4% increase in the evaluation function value while maintaining stability. Taking into consideration the aerodynamic performance of the chamber along with the topology optimization results, topology optimization of the negative pressure chamber was performed. The structure and arrangement of reinforcing ribs inside the chamber were obtained, with the reinforcing ribs connected to the wheel support arm designed in “八”-shaped and linear hollow structures. This optimization reduced the maximum vertical displacement of the negative pressure chamber to 18.5% of the original model, with a minimal increase in mass of 16.9%. It shows that the precise layout effect of the strengthening rib is obvious, and the vertical deformation is successfully controlled within a reasonable range. Finally, a prototype was constructed using UTR6180 photosensitive resin and 3D printing technology, with approximate dimensions of 300 mm×280 mm×15 mm and a mass of approximately 1.15 kg. The performance test of the prototype was conducted under various working conditions, demonstrating that the wall-climbing robot can stably adsorb and move on various bridge walls without slipping or drifting.

Stockwell Transform Combined with Subdomain Adaptation for Escalator Motor Bearing Transfer Diagnosis



CHEN Zhong, TANG Xin, ZHANG Daming, et al

2023, 51(12):  34-41.  doi:10.12141/j.issn.1000-565X.220758

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In the absence of sufficient escalator motor bearing failure data, to address the issue of unstable bearing fault characteristics during frequent load and speed variations in escalator operation, this paper proposed a transfer diagnosis method for escalator motor bearings using Stockwell (S) transformation combined with subdomain adaptation. Firstly, for the fault characteristics of escalator motor bearings, a time-frequency image of vibration signals was generated using the S transform combined with bilinear interpolation. This time-frequency image effectively reflects bearing fault features and is subsequently aligned with the requirements of the feature extraction network. Secondly, local maximum mean discrepancy (LMMD) was introduced at the output end of the feature extraction network layer based on the deep residual neural network ResNet-50. It incorporates the confidence of bearing fault sample categories as weights in the mapped maximum mean discrepancy (MMD), aligning the dis-tributions of subdomains belonging to the same category, thereby expanding the scope of transfer learning. Next, the network was constructed to minimize both LMMD and cross-entropy loss functions, and network training was performed using mini-batch gradient descent. Consequently, by refining the feature differences between different fault categories, fault subdomain self-adaptation was achieved, overcoming the problem of low transfer diagnosis accuracy. Finally, based on two publicly available bearing fault datasets and a limited amount of escalator motor bearing fault data, the S-transformed time-frequency dataset was constructed, and transfer diagnosis experiments were conducted. The results demonstrate that the proposed method achieves an average accuracy of 99.1% and 95.49% for transfer diagnosis in two different source-to-target domain scenarios of escalator bearings, outper-forming five commonly used diagnostic methods in terms of recognition accuracy and robustness.

Graph Neural Network for Fault Diagnosis with Multi-Scale Time-Spatial Information Fusion Mechanism



ZHAO Rongchao, WU Baili, CHEN Zhuyun, et al

2023, 51(12):  42-52.  doi:10.12141/j.issn.1000-565X.220593

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Due to the long-term operation of planetary gearboxes in strong noise environments and changing working conditions, the collected vibration signals exhibit weak fault characteristics and variable signal patterns, making them difficult to identify. Intelligent fault diagnosis of planetary gearboxes under these conditions remains a challenging task. In order to achieve high diagnostic accuracy and strong model generalization performance, a fault diagnosis method using a graph neural network with a multi-scale time-spatial information fusion mechanism is proposed. The method first uses convolution kernels of different scales to extract features from the original vibration signal, reducing the masking effect of strong noise signals on valuable information and enhancing its feature expression ability. A channel attention mechanism is then constructed to adaptively assign different weights among different channels to features of different scales, enhancing features in segments of information containing crucial fault characteristics. Finally, the multi-scale features of the convolution module output are used to construct graph data with spatial structure information for graph convolution learning. This approach allows for the full utilization and deep fusion of multi-dimensional time domain information and spatial correlation information, effectively improving the accuracy of diagnosis and the generalization performance of the model. The proposed method was verified using a fault dataset of wind power equipment with planetary gearbox structure. The average diagnosis accuracy of the proposed method was found to reach 98.85% and 91.29% under cross-load and cross-speed conditions, respectively. These results are superior to other intelligent diagnosis methods, including deep convolutional neural networks with wide first-layer kernels (WDCNN), long short-term memory network (LSTM), residual network (ResNet), and multi-scale convolution neural network (MSCNN). Therefore, the strong generalization performance and superiority of the proposed method were confirmed.

Structural Optimization and Flow Field Characteristics of Large Format SLM Forming Bin



LIU Guoyong, ZHANG Wenpeng, ZHANG Tongxin, et al

2023, 51(12):  53-63.  doi:10.12141/j.issn.1000-565X.220832

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To study the distribution of soot particles and the efficiency of fume extraction inside the SLM (selective laser melting) forming bin, this research analyzed the flow law of shielding gas and soot particles in the forming bin with Fluent discrete phase model (DPM) based on the established model of the large-format porous wind wall forming bin. Then multi-objective genetic algorithm (MOGA) was used to optimize the structure of the porous wind wall forming bin. The length of air inlet P₁, the radius of wind wall hole P₂, the length of conical protection plate P₃ and the shaft length of wind wall hole P₄ were taken as optimization variables, and the average flow velocity of shielding gas in the middle section of the forming bin, the concentration difference of smoke particles at the inlet and outlet of the forming bin and the maximum concentration of smoke particles in the entire forming bin were taken as optimization objectives. The response surfaces and sensitivity analysis results of each optimization variable and optimization target were obtained, and the optimization target before and after optimization was compared. The results show that for the four optimized variables, the order of influence on the flow velocity of the shielding gas in the middle section of the forming bin is P₂>P₄>P₃>P₁; the order of influence on the concentration difference of inlet and outlet particles is P₂>P₄>P₁>P₃; the order of influence on the maximum particle concentration in the porous air wall forming bin is P₂>P₁>P₃>P₄; the pore size of the porous wind wall plays a key role in the flow of dust particles in the forming bin. Through multi-objective genetic algorithm, the optimized length of gas inlet is 358 mm, the radius of wind wall hole is 20 mm, the length of conical protection plate is 589 mm, and the shaft length of wind wall hole is 6 mm. Compared with that before the optimization of the structure of the forming bin, the flow velocity of the shielding gas in the middle section of the forming bin after the optimization increases by 11.3%; the concentration difference between the inlet and outlet particles decreases by 16.8%; the maximum particle concentration in space decreases by 23.9%; the trend of outward diffusion of soot particles decreases, and the flow velocity of the shielding gas passing 30 mm above the forming table increases by 21%, indicating that the shielding gas can carry the soot particles out of the forming bin more efficiently.

Traffic Sign Detection Based on Channel Attention and Feature Enhancement



LUO Yutao, GAO Qiang

2023, 51(12):  64-72.  doi:10.12141/j.issn.1000-565X.220639

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Traffic signs on the road contain a large amount of semantic information about traffic rules, and rapid and accurate access to this information helps to achieve higher levels of assisted driving functions, thus improving vehicle’s safety performance. In view of the traffic signs are susceptible to external factors and the problems of high similarity between categories and small size, this paper made targeted improvements in data augmentation, feature extraction and feature enhancement based on YOLOv5s model. In the data augmentation part, color space transformation and geometric transformation matrix were used to simulate the possible color changes and shape changes of traffic signs in actual scenes, and the Mosaic algorithm and Copy-paste algorithm were used to improve the number of tiny traffic signs in the training set and the richness of the background. In the feature extraction part, a feature extraction module based on channel attention calibration was constructed to improve the model’s ability to discriminate similar features. In the feature enhancement part, the number of prediction branches and downsampling multiplier were optimized by fusing shallow features and deep features with a dual-path enhancement structure, so as to increase the detection accuracy of tiny traffic signs. In addition, the K-means++ algorithm was used to cluster the prior bounding box templates and construct the loss function based on the CIoU metric, thus reducing the difficulty of the prior bounding box regression. Experiments on the TT100K and CCTSDB dataset test show that the mAP@0.5 of the proposed model is 88.8% and 83.5% respectively, and the speed of the model is 120.5 f/s and 114.7 f/s respectively. Compared with the existing traffic sign detection models, the proposed model reaches the advanced level in both accuracy and speed. Comparison experiments for data augmentation algorithms, prediction branches, and channel attention module positions further demonstrate the effectiveness of the proposed specific optimization methods.

Energy，Power & Electrical Engineering

Axial Temperature Rise Characteristics of Overhead Line Broken Strand Defects and Identification Method



JIANG Xingliang, HUANG Wuhong, LIAO Yi, et al

2023, 51(12):  73-82.  doi:10.12141/j.issn.1000-565X.230035

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Defects in operating outdoor overhead transmission lines with broken strands will cause local excess temperature rise. The maximum temperature occurs at the defect and decays rapidly to a defect-free area. Infrared thermography based on temperature distribution can identify the degree of broken strands defects. However, the wind speed will significantly reduce the surface temperature of the observed object, making infrared detection difficult. To study the axial temperature distribution at the broken strand area of overhead lines under low wind speed, the paper took the LGJ-240/30 type steel-cored aluminum strand as an example, and conducted thermal cycling tests in the State Key Laboratory of Power Transmission Equipment & System Security and New Technology of Chongqing University. The broken strand defect was produced by manual destruction, the homemade wind speed was regulated by the air collecting device, and the AC large current generator provided a stable Joule heat source. The influence of the number of broken strands on the maximum temperature rise at the back of the defect and the temperature difference in the axial defect-free area was obtained. And based on this, the infrared identification method of broken strand number when the wind speed is 1~3 m/s was proposed. Finally, the method was verified by natural experiments in the National Field Science Observation and Research Station. The results show that after the occurrence of strand breaks in overhead transmission lines, the axial temperature difference between the extreme value of the defective temperature and the normal temperature in the non-defective area decreases rapidly with the increase of the wind speed; the fitting coefficient b, which describes the heat transfer term in the fitting equation of the axial temperature difference θ and the wind speed u, increases with the increase of current carrying capacity and the number of strand breaks. The proposed method has a recognition rate of more than 90.1% for the number of broken strands and more than 94% for defects under the condition of low wind speed, the load current is 360, 480, and 600 A, and the number of broken strands is more than 3. It solves the problem of infrared thermal inspection project under low wind speed without missing the best maintenance time, greatly improves the maintenance efficiency of line maintenance, guarantees the safe and stable operation of power grid, and has guiding significance for the infrared thermal inspection project of overhead transmission lines.

Scheduling Strategies for Electric Vehicle Participation in Electricity Markets Under Multi-Network Collaboration



DONG Ping, WEI Shuyang, LIU Mingbo

2023, 51(12):  83-94.  doi:10.12141/j.issn.1000-565X.220822

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In view of the time-space uncertainty of electric vehicle charging demand, how to participate in the electricity market and how to maximize the operating profit has become a problem that needs to be solved. Firstly, this study established an electric vehicle travel prediction model based on multi-layer deep learning algorithm. The multilayer perceptron and long short-term memory network were used to learn the travel data and road condition data of electric vehicles, and the travel behavior and road condition of the next day were predicted by the trained prediction model. Secondly, considering the influence of the variability of road conditions on the prediction accuracy, the future path rolling optimization method and the speed-energy consumption model were used to simulate the travel behavior of electric vehicles the next day, so as to obtain more accurate time-space state and charge state of electric vehicles. Finally, considering the coordinated scheduling of the energy market, the charging and discharging behavior of electric vehicles in different periods was planned through the charging and discharging scheduling model of the day-ahead market to maximize the interests of electric vehicle agents. In order to prove the accuracy of the proposed prediction method, it was compared with the commonly used Monte Carlo method and Latin hypercube method. The results show that the deep learning algorithm proposed in this study has higher accuracy. The model was applied to the IEEE33 node test system for verification. The experimental results show that the peak-valley difference of the power system can be effectively reduced under the scheduling of electric vehicle agents. In the case of system congestion, the problem of system line congestion can be alleviated by changing the scheduling strategy of electric vehicles. The agent’s revenue and the user’s travel cost were analyzed. The results show that under the agent’s scheduling, it can not only increase the income of agents, but also reduce the travel cost of users, and achieve a win-win situation.

Deflection Magnetic Field Analysis and Deflection Torque Calculation of a Hybrid Excitation Multi-Degree of Freedom Spherical Motor



CAO Jianghua, ZENG Bingsen, YANG Xiangyu, et al

2023, 51(12):  95-106.  doi:10.12141/j.issn.1000-565X.220610

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In order to solve the problems of small torque, large torque fluctuation and small motion range in the deflection motion part of the existing multi-degree-of-freedom motor, a hybrid excitation multi-degree of freedom spherical motor was proposed. After introducing the basic structure and operation principle of the motor, this paper proposed a magnetic field analysis method based on multi-node Schwarz-Christoffel (S-C) transform to solve the problem of slow speed in the magnetic field calculation of the motor when using the three-dimensional (3D) finite element method. This method uses discrete line currents to be equivalent to the permanent magnets and windings of the motor, and converts the complex air gap region into the torus region through multi-node S-C transform and exponential transform, respectively. And it uses the analytical solution of the scalar position in the torus region to calculate the magnetic field information in the current region of a single line, and calculates the overall magnetic field information by superposition. Then, the magnetic field information of the original air gap region was calculated by using the conversion relationship of the coordinate points in each region. After calculating the radial magnetic density, the electromagnetic torque of the deflected part was calculated by Ampere’s law. In addition, the analytical method and the three-dimensional finite element method were used to model and analyze the magnetic field characteristics and deflection torque characteristics of the motor. Through comparison, it is found that the magnetic field calculation results of the two methods are in good agreement, the maximum errors in amplitude of the analytical method relative to the finite element method are 7.3% and 3.92%, respectively,which verifies the accuracy of the analytical method. In order to further verify its validity, a prototype and an experimental platform were developed to test the static deflection torque of the motor. The results show that the motor has a high deflection torque, and the maximum deflection torque is 2.13 N·m. The errors between the experimental results and the calculation results of the finite element method and the analytical method are 22.62% and 20.13%, respectively, which verifies the effectiveness of the analytical method modeling.

Kinetic Mechanism of Pyrolysis of Lilac Lignin Dimer Memes



LOU Bo, LI Senhao, LU Song, et al

2023, 51(12):  107-117.  doi:10.12141/j.issn.1000-565X.220555

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Lilac-based lignin is an important lignin. Most of the natural lignins are connected through β-O- 4 bonds to form a reticular structure. β-O- 4-type lilac-based lignin dimeric modulators are closer to the actual lilac-based lignin structure, as multiple methoxy groups were added to it on the basis of the modulators studied by the previous researchers. In the Dmol³ module of the software Materials Studio 2019, the pyrolysis reaction paths of β-O- 4 lilac-based lignin dimer modulators were simulated based on the density flooding theory using the B3LYP hybridization flooding at 875 K and 101 kPa. The enthalpy values of the reactants and products were calculated for each step of the reaction, and the frequency analysis was carried out by the Vibration Analysis module to confirm that there were only real frequencies and no imaginary frequencies; the enthalpy change of each step of the reaction was calculated and the total enthalpy change of the reaction paths was compared. The smaller the total enthalpy change was, the easier the paths were to take place thermodynamically, and then the more advantageous reaction paths were obtained to get the pyrolysis products of the corresponding paths finally. The results show that the initial pyrolysis of β-O- 4 lilac-based lignin dimer modulators at 875 K and 101 kPa is more likely to involve the breakage of the C _α —C _β bond and the β-O- 4 bond, among which the breakage of the β-O- 4 bond is the most likely to occur. The more favorable reaction paths include R4 with a total enthalpy change of -59.65 kJ/mol, R10 with a total enthalpy change of -219.44 kJ/mol, R12 with a total enthalpy change of -14.93 kJ/mol, R21 with a total enthalpy change of -389.29 kJ/mol, R23 with a total enthalpy change of -466.24 kJ/mol, and R24 with a total enthalpy change of -276.72 kJ/mol, with the most favorable paths being R21, R23 and R24.The main products of pyrolysis are o-benzenetriol, 3,4,5-trihydroxybenzyl alcohol, 3,4,5-trihydroxybenzaldehyde and ethanol, among which o-benzenetriol, 3,4,5-trihydroxybenzyl alcohol and ethanol are the pyrolysis products of R21, R23 and R24. The simulation results obtained in this study can lay the foundation for further simulation calculations for generating biomass coke.

Food Science & Technology

Study on Beneficial Characteristics inVitro of Enterococcus Faecalis EF-ZA1107-06



XU Xilin, PENG Yunyan, ZHOU Xiaoli, et al

2023, 51(12):  118-130.  doi:10.12141/j.issn.1000-565X.220798

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Studies have shown that probiotic has good antidiabetic and antioxidant activities and great inhibitory effects on human cancer cell lines. This paper took α-glucosidase and α-amylase inhibition rate, superoxide anion radical scavenging rate, ferrous ion chelating rate and reducing activity as indexes, and compared the antidiabetic and antioxidant capacities in vitro between Enterococcus faecalis EF-ZA1107-06 and Lactobacillus rhamnosus GG (LGG). The effects of EF-ZA1107-06 on cellular oxidative stress were investigated by measuring the enzyme activities of glutathione peroxidase (GSH-Px), total superoxide dismutase (T-SOD) and catalase (CAT) in Caco-2 cell lines. The anticancer activity and mechanism were also preliminarily explored using HepG2 and MDA-MB-231 cell lines. The results show that the supernatant of EF-ZA1107-06 has the highest scavenging rate of superoxide anion free radical from 40.78% to 59.61% and strongest ferrous ion chelating ability from 39.28% to 56.59%, while the lysate of EF-ZA1107-06 has the highest reducing activity, equivalent to 1.072 mmol/L equivalent cysteine, which is remarkably higher than that of LGG lysate (P<0.05). The inhibition of α-amylase and α-glucosidase activity confirms the antidiabetic activity of EF-ZA1107-06. Meanwhile, EF-ZA1107-06 can reduce oxidative damage caused by H₂O₂ to Caco-2 cells to a certain extent and inhibit the S phase and G2 phase of HepG2 cells, but only the S phase of MDA-MB-231 cells. The induction of the early apoptosis of HepG2 cells and MDA-MB-231 cells confirms the anticancer activity of EF-ZA1107-06.

Inhibitory Effect of Sertraline on Vibrio Parahaemolyticus



MENG Hecheng, HE Changheng, CAI Pingyao, et al

2023, 51(12):  131-139.  doi:10.12141/j.issn.1000-565X.230183

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Recently, there have been increasing reports on the emergence of antibiotic resistance in environmental and seafood isolates of Vibrio parahaemolyticus. Therefore, it is urgent to find strategies to replace traditional antimicrobial agents for antibacterial control of Vibrio parahaemolyticus contamination and infection. In this study, the antibacterial activity and anti-biofilm formation ability of sertraline against Vibrio parahaemolyticus were evaluated through minimum inhibitory concentration (MIC) test, minimum bactericidal concentration (MBC) test, growth curve test, motivity test, and crystal violet staining test, and transmission electron microscope observation of the morphological changes of Vibrio parahaemolyticus. Meanwhile, the attenuating effect of sertraline on Vibrio parahaemolyticus was explored by detecting and analyzing the influence of sertraline on the transcription of virulence genes of Vibrio parahaemolyticus. The results indicate that the MIC of sertraline on Vibrio parahaemolyticus is 32 μg/mL and the MBC is 64 μg/mL, which are able to damage the cell membrane and cell wall of Vibrio parahaemolyticus. Furthermore, sertraline can lead to shrinkage and shriveling at MIC concentration, and rupture and leakage of intracellular substances at MBC concentration. Sertraline can significantly inhibit the swimming and swarm motion of Vibrio parahaemolyticus with the inhibition rates of 88.6% and 71.5%, respectively, at the subinhibitory concentration. The ability of sertraline to inhibit biofilm formation of Vibrio parahaemolyticus shows a linear correlation of sertraline’s concentration, and the inhibition rates of 8, 16, 32 and 64 μg/mL sertraline on the biofilm formation of Vibrio parahaemolyticus ATCC17802 are 66.9%, 79.5%, 88.3% and 89.3%, respectively. Sertraline can significantly inhibit the expression of virulence genes fliA, ompW and aphA of Vibrioparahaemolyticus with inhibition rates of 71.9%, 88.7% and 77.3% at subinhibitory concentration, respectively. The results show that sertraline has a good antibacterial activity and anti-biofilm formation ability against Vibrio parahaemolyticus, and it can reduce the transcription level of virulence genes under the allowable concentration of plasma without affecting its growth.

Two Visual and Rapid Enzymatic Recombinase Amplification Methods for GI and GII Noroviruses Detection



YANG Yange, WU Zhanwen, LI Tao, et al

2023, 51(12):  140-151.  doi:10.12141/j.issn.1000-565X.220529

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Norovirus (NoV) is one of the common foodborne viruses, and even a small amount of NoV can lead to infection and illness. However, there are currently no specific therapeutic drugs and vaccines available, making it crucial to establish a rapid detection method for early screening of NoV in food products. This study first wrapped the target genes of GⅠ and GⅡ NoV specified in GB 4 789.42 into the capsid protein of bacteriophage MS2 with armored RNA technology, creating recombinant plasmid reference samples containing both GⅠ and GⅡ NoV targets. Next, based on the Enzymatic Recombinase Amplification (ERA) technology, the primers and probes of basic and fluorescence ERA detection were designed for both GⅠ and GⅡ NoV, and the optimal primers and probes were screened through experiments. Then two visualization methods for GI and GII NoV detection were established, namely ERA chromogenic and fluorescence method, and the results can be observed with the naked eye. Furthermore, the reaction program was optimized, reducing the amplification time to 5 minutes and 8 minutes for both ERA chromogenic and fluorescence methods, respectively. By optimizing the reaction system, the volume was halved, thereby reducing the cost of detection. Under these conditions, the lowest sensitivity was 10^-2、10^-3 ng/μL for the recombinant plasmid reference sample, respectively. Finally, the established visual rapid detection method was applied to the detection of GⅠ and GⅡ NoV authentic specimens. And the performance parameters of the method were analyzed. The results show that the established GⅠ and GⅡ NoV ERA visual rapid detection method has good specificity, with no cross-amplification with other foodborne viruses, and can detect as low as 10 copies/μL of NoV. It meets the requirements for visual rapid screening of GⅠ and GⅡ NoV. The establishment of this method provides good technical support for the rapid screening and risk monitoring of NoV, and it is of great significance for controlling NoV outbreaks and safeguarding public health.

Extracting Oil from Diacylglycerol Microcapsules with Bromelain



LAN Dongming, WAN Chufeng, CHEN Ying, et al

2023, 51(12):  152-158.  doi:10.12141/j.issn.1000-565X.220145

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Diacylglycerol microcapsule is a new type of functional food ingredient. In order to accurately determine the oil content, peroxide value, diacylglycerol content and other physical and chemical indexes of diacylglycerol microcapsule, the optimal extraction method for the oil components should be established. In this study, four methods of acid hydrolysis, alkali hydrolysis, ultrasonic treatment and bromelain hydrolysis were used for wall-breaking pretreatment of flaxseed diacylglycerol microcapsule. The effects of the different pretreatment methods on the extraction of fats and oils from the microcapsules were compared. In other words, the extraction rate of the fats and oils and the peroxide value of the them were investigated, and the glyceride composition of the extracted fats and oils was analyzed by high performance liquid chromatography. The results show that: the diacylglycerol obtained by the bromelain hydrolysis method has the least loss and the lowest peroxide value; the diacylglycerol obtained by acid hydrolysis and ultrasonic treatment has higher extraction rate, but acid hydrolysis results in the greatest loss of diacylglycerol content and ultrasonic treatment results in the highest peroxide values; and the alkali hydrolysis treatment leads to the lowest oil extraction rate. On the basis of the single factor experiment of enzyme hydrolysis, orthogonal optimization test was performed to explore the effect of enzyme addition, temperature and time on oil extraction rate, and the extraction solvent after enzymatic digestion was also optimized. The results show that when the dosage of bromelain is 12 000 U/g, the enzymatic hydrolysis temperature is 55 ℃, and the enzymatic hydrolysis time is 5 min, the oil extraction rate of the diacylglycerol microcapsules can reach (98.60±0.78)%, and the peroxide value of the extracted oil is (0.037±0.001) g/100 g. The optimal extraction system after enzymatic hydrolysis is ethanol-diethyl ether-petroleum ether (2:5:5, in volume ratio). This method has high oil extraction rate and little impact on oil quality, providing theoretical basis for the detection of physicochemical properties of diacylglycerol microcapsules.